Crate streaming_algorithms

Performant implementations of various streaming algorithms.

Crates.io │ Repo

This library is a work in progress. PRs are very welcome! Currently implemented algorithms include:

• Count–min sketch
• Top k (Count–min sketch plus a doubly linked hashmap to track heavy hitters / top k keys when ordered by aggregated value)
• HyperLogLog
• Reservoir sampling

A goal of this library is to enable composition of these algorithms; for example Top k + HyperLogLog to enable roughly SELECT key FROM table GROUP BY key ORDER BY COUNT(DISTINCT value) DESC LIMIT k.

See this gist for a good list of further algorithms to be implemented. Other resources are Probabilistic data structures – Wikipedia, DataSketches – A similar Java library originating at Yahoo, and Algebird – A similar Java library originating at Twitter.

As these implementations are often in hot code paths, unsafe is used, albeit only when necessary to a) achieve the asymptotically optimal algorithm or b) mitigate an observed bottleneck.

Structs

CountMinSketch	An implementation of a count-min sketch data structure with conservative updating for increased accuracy.
HyperLogLog	An implementation of the HyperLogLog data structure with bias correction.
SampleTotal	Given population and sample sizes, returns true if this element is in the sample. Without replacement.
SampleUnstable	Reservoir sampling. Without replacement, and the returned order is unstable.
Top	This probabilistic data structure tracks the n top keys given a stream of (key,value) tuples, ordered by the sum of the values for each key (the "aggregated value"). It uses only O(n) space.
TopIter	An iterator over the entries and counts in a Top datastructure.

Traits

Intersect	Intersect zero or more &Self to create Option<Self>.
New	New instances are instantiable given a specified input of <Self as New>::Config.
UnionAssign	Union Self with Rhs in place.